Automatic determination of the threshold of an evoked neural response

ABSTRACT

Automatically analyzing neural activity. The method comprises: applying electrical stimulation to a target neural region at an initial current level that approximates a typical threshold-Neural Response Telemetry (NRT) level; recording an NRT measurement of neural activity within the target neural region in response to the stimulation; and utilizing a machine-learned expert system to predict, based on one or more features of the neural activity, whether the NRT measurement contains a neural response.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national stage application under 35 USC §371(c) ofPCT Application No. PCT/US2005/21207, entitled “Automatic Determinationof the Threshold of an Evoked Neural Response,” filed Jun. 15, 2005,which claims the priority of Australian Patent No. 2004903254 entitled,“Method and System for Measurement of Neural Response,” filed Jun. 15,2004. The above applications are hereby incorporated by reference hereinin their entireties.

The present application makes reference to the following patents andpatent applications: U.S. Pat. Nos. 4,532,930, 5,758,651, 6,537,200,6,565,503, 6,575,894 and 6,697,674, WO 2002/082982 and WO 2004/021885,which are hereby incorporated by reference herein in their entirety.

BACKGROUND

1. Field of the Invention

The present invention relates generally to the measurement of a neuralresponse evoked by electrical stimulation and, more particularly, to theautomatic measurement of an evoked neural response.

2. Related Art

Hearing loss, which may be due to many different causes, is generally oftwo types, conductive and sensorineural. In some cases, a person mayhave hearing loss of both types. Conductive hearing loss occurs when thenormal mechanical pathways for sound to reach the hair cells in thecochlea are impeded, for example, by damage to the ossicles. Conductivehearing loss is often addressed with conventional auditory prosthesescommonly referred to as hearing aids, which amplify sound so thatacoustic information can reach the cochlea.

In many people who are profoundly deaf, however, the reason for theirdeafness is sensorineural hearing loss. This type of hearing loss is dueto the absence or destruction of the hair cells in the cochlea whichtransduce acoustic signals into nerve impulses. Those suffering fromsensorineural hearing loss are thus unable to derive suitable benefitfrom conventional hearing aids due to the damage to or absence of themechanism for naturally generating nerve impulses from sound.

It is for this purpose that another type of auditory prosthesis, aCochlear™ implant (also commonly referred to as Cochlear™ prostheses,Cochlear™ devices, Cochlear™ implant devices, and the like; generallyand collectively referred to as “cochlear implants” herein) has beendeveloped. These types of auditory prostheses bypass the hair cells inthe cochlea, directly delivering electrical stimulation to the auditorynerve fibers via an implanted electrode assembly. This enables the brainto perceive a hearing sensation resembling the natural hearing sensationnormally delivered to the auditory nerve.

Cochlear implants have traditionally comprised an external speechprocessor unit worn on the body of the recipient and areceiver/stimulator unit implanted in the mastoid bone of the recipient.The external speech processor detects external sound and converts thedetected sound into a coded signal through an appropriate speechprocessing strategy. The coded signal is sent to the implantedreceiver/stimulator unit via a transcutaneous link. Thereceiver/stimulator unit processes the coded signal to generate a seriesof stimulation sequences which are then applied directly to the auditorynerve via a series-arrangement or an array of electrodes positionedwithin the cochlea.

More recently, the external speech processor and implanted stimulatorunit may be combined to produce a totally implantable cochlear implantcapable of operating, at least for a period of time, without the needfor an external device. In such an implant, a microphone would beimplanted within the body of the recipient, for example in the ear canalor within the stimulator unit. Detected sound is directly processed by aspeech processor within the stimulator unit, with the subsequentstimulation signals delivered without the need for any transcutaneoustransmission of signals.

Generally, there is a need to obtain data from the implanted componentsof a cochlear implant. Such data collection enables detection andconfirmation of the normal operation of the device, and allowsstimulation parameters to be optimized to suit the needs of individualrecipients. This includes data relating to the response of the auditorynerve to stimulation, which is of particular relevance to the presentinvention. Thus, regardless of the particular configuration, cochlearimplants generally have the capability to communicate with an externaldevice such as for program upgrades and/or implant interrogation, and toread and/or alter the operating parameters of the device.

Determining the response of an auditory nerve to stimulation has beenaddressed with limited success in conventional systems. Typically,following the surgical implantation of a cochlear implant, the implantis fitted or customized to conform to the specific recipient demands.This involves the collection and determination of patient-specificparameters such as threshold levels (T levels) and maximum comfortlevels (C levels) for each stimulation channel. Essentially, theprocedure is performed manually by applying stimulation pulses for eachchannel and receiving an indication from the implant recipient as to thelevel and comfort of the resulting sound. For implants with a largenumber of channels for stimulation, this process is quite time consumingand rather subjective as it relies heavily on the recipient's subjectiveimpression of the stimulation rather than any objective measurement.

This approach is further limited in the case of children andprelingually or congenitally deaf patients who are unable to supply anaccurate impression of the resultant hearing sensation, and hencefitting of the implant may be sub-optimal. In such cases anincorrectly-fitted cochlear implant may result in the recipient notreceiving optimum benefit from the implant, and in the cases ofchildren, may directly hamper the speech and hearing development of thechild. Therefore, there is a need to obtain objective measurements ofpatient-specific data, especially in cases where an accurate subjectivemeasurement is not possible.

One proposed method of interrogating the performance of an implantedcochlear implant and making objective measurements of patient-specificdata such as T and C levels is to directly measure the response of theauditory nerve to an electrical stimulus. The direct measurement ofneural responses, commonly referred to as Electrically-evoked CompoundAction Potentials (ECAPs) in the context of cochlear implants, providesan objective measurement of the response of the nerves to electricalstimulus. Following electrical stimulation, the neural response iscaused by the superposition of single neural responses at the outside ofthe axon membranes. The measured neural response is transmitted to anexternally-located system, typically via a telemetry system. Such NeuralResponse Telemetry (NRT™) provides measurements of the ECAPs from withinthe cochlea in response to various stimulations. The measurements takento determine whether a neural response or ECAPs has occurred arereferred to herein by the common vernacular NRT measurements. Generally,the neural response resulting from a stimulus presented at one electrodeis measured at a neighbouring electrode, although this need not be thecase.

A sequence 100 of NRT measurements 102 is shown in FIG. 1A. Sequence 100contains seven NRT measurements 102A-102G which display a good neuralresponse. Each NRT measurement waveform 102A-102G comprises a clearnegative peak (N1) 104 and positive peak (P1) 106. Only one positive andnegative peak is shown in FIG. 1A for clarity. As used herein, a “good”neural response is one which approximates a true neural response to anapplied stimulus current.

An NRT measurement waveform may have a partial N1 peak, no P1 peak or adouble positive peak P1 and P2 and still represent a good neuralresponse. The measurement waveforms 102 toward the top of the graphdepicted in FIG. 1A (measurement waveforms 102A, 102B, for example)indicates a stronger neural response to a relatively large neuralstimulus, while the measurement waveforms toward the bottom of the graph(measurement waveforms 102F and 102G, for example) indicate a weakerneural response with reduced neural stimuli strengths.

Two sequences 120A and 120B of seven (7) NRT measurements 122A-122G thatdisplay the absence of a neural response are shown in FIG. 1B. In theleft-hand sequence 120A, stimulus artifact and/or noise are observed.The stimulus artifact may give the impression of artificial peaks whichmay be interpreted as a neural response to a previously applied stimulussignal. In right-hand sequence 120B, noise is observed.

Distinguishing between measurements that display a neural response suchas those of FIG. 1A, and measurements which do not display a neuralresponse such as those of FIG. 1B, is an important aspect of performingNRT measurements. This task can be extremely difficult, for instancewhen the combination of stimulus artifact and noise gives the appearanceof a weak neural response.

In particular, the minimum stimulus current level required to evoke aneural response at a given electrode is referred to herein as thethreshold NRT level, or T-NRT. In general, T-NRT profiles are correlatedwith MAP T and C profiles, and thus T-NRT levels can be used as a guidefor MAP fitting. Accordingly, accurate determination of T-NRT values foreach electrode and for each recipient is highly desirable.

One conventional approach to determine T-NRT values is the AmplitudeGrowth Function (AGF) method. The AGF method is based on the premisethat the peak-to-peak amplitude of a neural response increases linearlywith stimulus current level. It should be appreciated, however, that therelationship is more accurately defined by a sigmoidal function. Byobtaining the value at different stimulus current levels, a regressionline may be drawn through these measurement points and extrapolated tothe point at which the peak-to-peak amplitude becomes zero, thusindicating the threshold stimulus level.

For example, FIG. 2 illustrates a typical, non-linear, measurement setof peak-to-peak amplitude (in microvolts) vs. current level (indigitized current level units). As is well known in the art, there is aone-to-one exponential relationship between the unit of current leveland the conventional unit of current (the ampere). In one embodiment,the current level scale is from 0 to 255 with each unit representing anincreasingly lager quantity of amperes. This single set of measurements200 (only one of which is referenced in FIG. 2 for ease of illustration)can be fitted with a number of regression lines 202A, 202B, and 202C,yielding possible T-NRT values of 125, 135 and 148 current level units,a variation of over 18%. This is because AGF is observer-dependent whenselecting the measurement points to include in regression.

In addition, the AGF approach requires a significant number of NRTmeasurements above the threshold to enable a regression line to bedetermined. Such measurements may be beyond the recipient's loudestacceptable or comfort level, and thus the ability to post-operativelyobtain such measurements is limited. Additionally, such measurements donot yield a simple linear relationship, and typically various regressionlines can be determined resulting in significantly different T-NRTlevels from a given measurement set.

Visual detection of T-NRT levels is a more fundamental conventionalapproach. NRT measurements of increasing stimulus level are performeduntil the stimulus level at which a neural response is detected, atwhich point the T-NRT level is defined as the stimulus level. Visualdetection depends critically on the acuity of the observer todistinguish between neural responses and artifact or noise. Visualdetection of threshold is also observer-dependent.

The presence of stimulus artifacts and noise in measurements of anevoked neural response can lead to an incorrect determination of whethera neural response, or ECAP, has occurred in the above conventionalsystems. Accordingly, there is a need to objectively and accuratelydetect T-NRT thresholds to facilitate neural response determinations.

Indeed, there is a need to accurately measure the response of nerves toelectrical stimulation in stimulating medical devices that deliverelectrical stimulation to other neural regions of a recipient such asthe central nervous system (including the brain and spinal cord), aswell as the peripheral nervous system (including the autonomic andsensory-somatic nervous systems). Thus, the accurate measurement of aneural response may provide a useful objective measurement of theeffectiveness of the stimulation in many applications.

SUMMARY

In one aspect of the invention, a method for automatically analysing anevoked neural response is disclosed. The method comprises: applyingelectrical stimulation to a target neural region at incrementallygreater current levels beginning with an initial current level that isas close as possible to a typical threshold-NRT level; recording an NRTmeasurement of an auditory signal which generated by the target neuralregion in response to the stimulation; and utilizing a machine-learnedexpert system to predict whether the NRT measurement contains a neuralresponse based on a plurality of features extracted from the auditorysignal.

In another aspect of the invention, a cochlear implant system isdisclosed. The system comprises: a cochlear implant configured tocommunicate with an external device; and an automatic neural responsemeasurement system, communicably coupled to the cochlear implant,configured to record Neural Response Telemetry (NRT) measurements ofneural activity within a cochlea in response to electrical stimulationof the cochlear by electrodes of the cochlear implant, wherein aninitial current level of a stimulus applied to evoke a neural responseis set to a level which is as close as possible to a typical thresholdcurrent level which would cause a neural response to occur, the systemcomprising an expert system configured to determine whether the NRTmeasurement contains a neural response based on a plurality of theextracted features.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a graph illustrating exemplary measurements obtained ofneural responses evoked by varying stimulus levels.

FIG. 1B is a graph illustrating exemplary measurements of neuralresponse showing stimulus artifact and noise.

FIG. 2 is a graph of peak-to-peak evoked neural response amplitude vs.stimulus current level, showing possible regression lines.

FIG. 3 is a perspective view of a cochlear implant system including acochlear implant coupled to an expert system in which embodiments of thepresent invention are advantageously implemented.

FIG. 4A is a high-level flow chart in accordance with one embodiment ofthe present invention.

FIG. 4B is a high-level flow chart in accordance with an alternativeembodiment of the present invention.

FIGS. 5A and 5B are a flowchart showing an algorithm by which a minimumstimulus threshold may be determined in accordance with an embodiment ofthe invention.

FIG. 5C is a flowchart showing an algorithm by which the stimuluscurrent level is incremented in accordance with an embodiment of theinvention.

FIG. 6A illustrates one embodiment of a decision tree used in theembodiment of FIG. 5A to determine whether a neural response has beenevoked.

FIG. 6B illustrates one embodiment of a decision tree used in theembodiment of FIG. 5B to determine whether a neural response has beenevoked.

FIG. 7 is a flowchart illustrating an automated algorithm foroptimization of a third phase compensatory stimulus in accordance withone embodiment of the present invention.

FIG. 8 is a flowchart illustrating the derivation of the minimumstimulus threshold T-NRT from MaxT-NRT and MinT-NRT values, inaccordance with one embodiment of the present invention.

FIG. 9 is a graph illustrating a predefined expected or ‘good’ neuralresponse used for comparison by a portion of the decision tree of FIG.6.

FIG. 10 is a graph illustrating a predefined expected or ‘good’ neuralresponse plus stimulus artifact used for comparison by a portion of thedecision tree of FIG. 6.

FIG. 11 illustrates a predefined expected or ‘good’ stimulus artifact.

DETAILED DESCRIPTION

The present invention is directed to automatically analyzing an evokedneural response to determine the threshold Neural Response Telemetry(T-NRT) while avoiding the above and other drawbacks of conventionalapproaches. Generally, the systems, methods, techniques and approachesof the present invention apply electrical stimulation to a target neuralregion at incrementally greater current levels beginning with an initialcurrent level that is as close as possible to a typical T-NRT level;record an NRT measurement of an auditory signal which generated by thetarget neural region in response to the stimulation; and utilize amachine-learned expert system to predict whether the NRT measurementcontains a neural response based on a plurality of features extractedfrom the auditory signal.

In one embodiment, the initial current level is selected to insuresafety while minimizing the quantity of measurements required todetermine the T-NRT. As such, in post-operative applications, theinitial current level is substantially below the typical T-NRT and at acurrent level at which a neural response is not expected to be evokedwhile in intraoperative applications, the initial current level is belowbut close to the typical T-NRT. Where it is evaluated by the decisiontree that a neural response has not been evoked, the amplitude orcurrent level of the neural stimulus is preferably incremented and themethod repeated. Such embodiments provide for the amplitude or currentlevel of the applied stimuli to be gradually increased until the expertsystem evaluates that a neural response has been evoked. The thresholdis then locally established, preferably at a finer stimulus resolution.

Advantageously, the present invention does not require making recordingsat supra-threshold stimulation levels; that is, the T-NRT values areobtained at a stimulation current level that rarely exceeds therecipient's maximum comfortable level of stimulation. Rather,embodiments of the present invention approach threshold from about thesame stimulation levels and stop as soon as a confident neural responseis established.

Another advantage of the present invention is the use of an expertsystem that considered a variety of extracted features. This is incontrast to the known conventional systems in which measurement of theneural response requires an expert operator to provide an assessment ofthe obtained neural response measurement based on both audiologicalskills and cumulative experience in interpretation of specific neuralresponse measurements. In contrast, the present invention analyzes themeasured neural responses automatically and accurately without thecontribution of an expert user.

Before describing the features of the present invention, it isappropriate to briefly describe the construction of a cochlear implantsystem with reference to FIG. 1.

FIG. 3 is a pictorial representation of a cochlear implant system inaccordance with one embodiment of the present invention. Cochlearimplant system 300 comprises a cochlear implant coupled to an automaticneural response measurement system in which embodiments of the presentinvention are advantageously implemented.

Referring to FIG. 3, the relevant components of outer ear 301, middleear 305 and inner ear 307 are described next below. In a fullyfunctional ear outer ear 301 comprises an auricle 310 and an ear canal302. An acoustic pressure or sound wave 303 is collected by auricle 310and channelled into and through ear canal 302. Disposed across thedistal end of ear cannel 302 is a tympanic membrane 304 which vibratesin response to acoustic wave 303. This vibration is coupled to ovalwindow or fenestra ovalis 312 through three bones of middle ear 305,collectively referred to as the ossicles 306 and comprising the malleus308, the incus 309 and the stapes 311. Bones 308, 309 and 311 of middleear 305 serve to filter and amplify acoustic wave 303, causing ovalwindow 312 to articulate, or vibrate. Such vibration sets up waves offluid motion within cochlea 316. Such fluid motion, in turn, activatestiny hair cells (not shown) that line the inside of cochlea 316.Activation of the hair cells causes appropriate nerve impulses to betransferred through the spiral ganglion cells and auditory nerve 314 tothe brain (not shown), where they are perceived as sound.

Conventional cochlear implant system 300 comprises external componentassembly 342 which is directly or indirectly attached to the body of therecipient, and an internal component assembly 344 which is temporarilyor permanently implanted in the recipient. External assembly 342typically comprises microphone 324 for detecting sound, a speechprocessing unit 326, a power source (not shown), and an externaltransmitter unit 328. External transmitter unit 328 comprises anexternal coil 330 and, preferably, a magnet (not shown) secured directlyor indirectly to the external coil. Speech processing unit 326 processesthe output of audio pickup devices 324 that are positioned, in thedepicted embodiment, by ear 310 of the recipient. Speech processing unit326 generates coded signals, referred to herein as a stimulation datasignals, which are provided to external transmitter unit 328 via a cable(not shown). Speech processing unit 326 is, in this illustration,constructed and arranged so that it can fit behind the outer ear 310.Alternative versions may be worn on the body or it may be possible toprovide a fully implantable system which incorporates the speechprocessor and/or microphone into the implanted stimulator unit.

Internal components 344 comprise an internal receiver unit 332, astimulator unit 320, and an electrode assembly 318. Internal receiverunit 332 comprises an internal transcutaneous transfer coil (not shown),and preferably, a magnet (also not shown) fixed relative to the internalcoil. Internal receiver unit 332 and stimulator unit 320 arehermetically sealed within a biocompatible housing. The internal coilreceives power and data from external coil 330, as noted above. A cableor lead of electrode assembly 318 extends from stimulator unit 320 tocochlea 316 and terminates in an array of electrodes 342. Signalsgenerated by stimulator unit 320 are applied by electrodes 342 tocochlear 316, thereby stimulating the auditory nerve 314.

In one embodiment, external coil 330 transmits electrical signals to theinternal coil via a radio frequency (RF) link. The internal coil istypically a wire antenna coil comprised of at least one and preferablymultiple turns of electrically insulated single-strand or multi-strandplatinum or gold wire. The electrical insulation of the internal coil isprovided by a flexible silicone molding (not shown). In use, implantablereceiver unit 332 may be positioned in a recess of the temporal boneadjacent ear 310 of the recipient.

Further details of a convention cochlear implant device may be found inU.S. Pat. Nos. 4,532,930, 6,537,200, 6,565,503, 6,575,894 and 6,697,674,which are hereby incorporated by reference herein in their entirety.

Speech processing unit 326 of cochlear implant system 300 performs anaudio spectral analysis of acoustic signals 303 and outputs channelamplitude levels. Speech processing unit 326 can also sort the outputsin order of magnitude, or flag the spectral maxima as used in the SPEAKstrategy developed by Cochlear Ltd.

The CI24M and CI24R model cochlear implants commercially available fromCochlear Ltd are built around the CIC3 Cochlear Implant Chip. The CI24REcochlear implant, also commercially available from Cochlear Ltd, isbuilt around the CIC4 Cochlear Implant Chip. Cochlear implants based oneither the CIC3 or CIC4 Cochlear Implant Chip allow the recording ofneural activity within cochlea 316 in response to electrical stimulationby the electrodes 342. Such Neural Response Telemetry (NRT) providesmeasurements of the Electrically-evoked Compound Action Potentials(ECAPs) from within cochlea 316. Generally, the neural responseresulting from a stimulus presented at one electrode 342 is measured ata neighbouring electrode 342, although this need not be the case.

As shown in FIG. 3, an automatic neural response measurement system 354is communicably coupled to speech processor 326 via a cable 352. System354 is, in one embodiment, a processor-based system such as a personalcomputer, server, workstation or the like, on which software programsare executed to perform the infra-threshold neural response measurementsof the present invention. In addition, system 354 comprises neuralresponse expert system(s) 350 that provide neural response thresholdpredictions in accordance with the teachings of the present invention.

An expert system 350 is a method of solving pattern recognitionproblems, based on classifications performed by a human expert of thepattern domain. By presenting a sample set of patterns and theircorresponding expert classifications to an appropriate computeralgorithm or statistical process, systems of various descriptions can beproduced to perform the recognition task. In preferred embodiments ofthe present invention the expert system comprises a machine learningalgorithm such as the induction of decision trees.

As one of ordinary skill in the art would appreciate, expert system(s)350 may be implemented in an external system such as system 354illustrated in FIG. 3. In alternative embodiments, expert systems 350may be implemented in speech processor 326 or in an implanted componentof a partially or totally-implanted cochlear implant.

FIGS. 4A and 4B are high-level flow charts of embodiments of the presentinvention. Referring first to FIG. 4A, infra-threshold process 400begins at block 402. At block 404 the initial stimulus current level isset to a level which is as close as possible to a typical thresholdcurrent level which would cause a neural response to occur. As noted,the initial current level varies with the anticipated environment(post-operative or intra-operative) and is also sufficiently high tominimize the number of NRT measurements which are to be performed. Inpost-operative applications the initial current level is below theanticipated T-NRT level while in intra-operative embodiments, theinitial current level is above or below the anticipated T-NRT level.

At block 406 the stimulation signal is applied to the target neuralregion and, at block 408 the neural response is measured or recorded.During and/or subsequent to the recording of the NRT measurement, aplurality of features are extracted from the NRT measurement at block410.

At block 412 an expert system is implemented to determine whether theNRT measurement contains a neural response. The expert system utilizes aplurality of the extracted features to make such a determination asdescribed herein. If a neural response has not occurred (block 414) thestimulus current level is increased and the above operations arerepeated. Otherwise, process 400 ceases at block 416.

In some embodiments, to avoid false positives, the amplitude or currentlevel of the neural stimulus is preferably successively incrementeduntil two consecutive neural stimuli have been applied both of whichlead to an evaluation by the expert system that a neural response hasbeen evoked. In such embodiments, the stimulus current level at whichthe first such neural response was evoked may be defined as a firstminimum stimulus threshold. In such embodiments, the current level of anapplied stimulus is preferably incrementally reduced from the firstminimum stimulus threshold, until two consecutive stimuli have beenapplied both of which lead to an evaluation by the expert system that aneural response has not been evoked. The higher of such stimuli currentlevels at which the neural response has not been evoked is preferablydefined as a second minimum stimulus threshold. Such an embodiment isillustrated in FIG. 4B, in which a second expert system is implementedto determine whether the NRT measurements taken during the descendingincrements contains a neural response. In one preferred embodiment, thesecond expert system is configured to minimize the overall error rate ascompared to the first expert system which may be configured to minimizethe occurrence of false positive events.

Such embodiments provide for the minimum stimulus threshold to bedefined with reference to the first minimum stimulus threshold and thesecond minimum stimulus threshold. For example, the minimum stimulusthreshold may be defined to be a current level closest to the average ofthe first minimum stimulus threshold and the second minimum stimulusthreshold. Alternately, in embodiments where the amplitude or currentlevel of the neural stimulus is successively incremented until twoconsecutive neural stimuli have been applied both of which lead to anevaluation by the decision tree that a neural response has been evoked,the minimum stimulus threshold may simply be defined to be equal to thestimulus current level at which the first such neural response wasevoked.

FIG. 5A is a flowchart illustrating the primary operations performed inone embodiment of the present invention. In this exemplary embodiment,the T-NRT level of a single electrode is measured.

Process 500 commences at block 502 and at bock 504 a stimulus currentlevel (CL) is initialized. To insure safety of the recipient, inpost-operative environments the initial current level is preferably alow value at which a neural response is not expected to be evoked.Specifically, the initial current level is set to a value that issignificantly below a typical threshold level (T-NRT). In one exemplaryembodiment, the initial current level is set to 100 post-operatively.However, in intra-operative environments, the noted safety concerns arenot applicable due to the lack of auditory response. As such, theinitial current level is set to a value that is below the typicalthreshold level (T-NRT). In one exemplary embodiment, the initialcurrent level is set to 160 intra-operatively. In both environments,however, the initial current level is not set to a value unnecessarilybelow the typical threshold level as to do so would increase the numberof NRT measurements that will be performed to reach the threshold levelwhich causes a neural response. It should also be appreciated that theinitial current level may have other values in alternative embodiments.Further, in alternative embodiments, the current level is user defined.

At block 506, a clinician is asked to accept the present value of thestimulus current level. The clinician may, for example, refuse ifprocess 500 is being applied post-operatively and the present value ofthe stimulus current level would exceed a recipient's comfort threshold.Refusal causes process 500 to cease at block 508. Additionally oralternatively, process 500 may be halted to avoid a violation ofelectrical capabilities of the components applying the neural stimulus.

Alternatively, clinician acceptance at block 506 leads to an NRTmeasurement being performed at block 510. The NRT measurement performedat block 510 involves application of a stimulus at the accepted stimuluscurrent level by at least one electrode of interest. Preferably, system354 implements a technique that removes or minimizes stimulus artifacts.For example, in one embodiment, system 354 implements a techniquesimilar to that described in U.S. Pat. No. 5,758,651, which is herebyincorporated by reference herein. This patent describes one conventionalapparatus for recovering ECAP data from a cochlear implant. This systemmeasures the neural response to the electrical stimulation by using thestimulus array to not only apply the stimulation but to also detect andreceive the response. In this system the array used to stimulate andcollect information is a standard implanted intra-cochlear and/orextra-cochlear electrode array. Following the delivery of a stimulationpulse via chosen stimulus electrodes, all electrodes of the array areopen circuited for a period of time prior to and during measurement ofthe induced neural response. Open circuiting all electrodes during thisperiod is to reduce the detected stimulus artifact measured with theECAP nerve response.

In an alternative embodiment, system 354 generates a compensatorystimulus signal in a manner such as that described in WO 2002/082982and/or WO 2004/021885, each of which is hereby incorporated by referenceherein. Following application of a first stimulus to a nerve, WO2002/082982 teaches application of a compensatory stimulus closelyafterwards to counteract a stimulus artifact caused by the firststimulus. In some such embodiments, automatic optimization of thecompensatory stimulus is performed thereby providing automatedcancellation or minimization of stimulus artifacts from measurements ofthe evoked neural response.

WO 2004/021885 relates to the control of a reference voltage of a neuralresponse amplifier throughout signal acquisition to avoid the amplifierentering saturation. While variation of the reference voltage causes theoutput of the amplifier to be a piecewise signal, such a piecewisesignal is easily reconstructed, and thus this disclosure allows anamplifier of high gain to be used to improve signal acquisitionresolution. In some such embodiments, a reference voltage of anamplifier used in the measurement process is altered during themeasurement in order to produce a piecewise signal which avoidssaturation of the amplifier.

A neural response to such a stimulus is measured or recorded by way ofan adjacent electrode and a high gain amplifier (not shown), to yield adata set of 32 voltage samples (not shown) which form the NRTmeasurement (also referred to as the NRT measurement waveform or traceherein).

Operations depicted in dashed block 505 are next performed to improverecording quality and, if the quality is poor, to cease recording theneural response at that electrode. At block 512, process 500 performs avoltage level compliance check to determine whether the implant candeliver the required stimulus current by providing sufficient electrodevoltage. If the compliance check determines that that an error hasoccurred, such as by reading a flag generated by the above-noted soundprocessor chips, cause process 500 ceases at block 514. However, if atblock 512 it is determined that the hardware is in compliance,processing continues at block 516.

At block 516 a check is made of whether clipping of the NRT amplifieroccurred. In one embodiment, this too may be determined by reading aBoolean flag generated by the above-noted sound processing chips. If NRTamplifier clipping occurred, then processing continues at block 518 atwhich the compensatory stimulus and/or the amplifier gain is optimized.The operations performed at block 518 are described in detail below withreference to FIG. 7. Processing then returns to block 510 and the aboveoperations are repeated.

At block 520, a machine-learned expert system is utilized to predictwhether an NRT measurement contains a neural response based on theplurality of extracted auditory signal features. In one embodiment, theexpert system was built using the induction of decision trees. In oneimplementation of such an embodiment, the induction of decision treesmachine learning algorithm is the algorithm C5.0 described in Quinlan,J., 1993. “C4.5: Programs for Machine Learning.” Morgan Kaufmann, SanMateo; and Quinlan, J., 2004. “See5: An Informal Tutorial.” RulequestResearch, both of which are hereby incorporated by reference herein.

In one embodiment, the decision tree 600A illustrated in FIG. 6A isapplied to the obtained 32 sample set measurement of the NRTmeasurement. That is, neural response expert system 350 considers orprocesses a plurality of features extracted from the NRT measurement todetermine if it contains a “good” neural response. As used herein, a“good” neural response is one which approximates a true neural responseto the applied stimulus level as determined by a sampling astatistically-significant population of recipients.

Should decision tree 600A determine that a given NRT measurement doesnot contain a “good” neural response and thus that a neural response hasnot been evoked (block 521), the process 500 continues at block 522 atwhich the stimulus current level CL is incrementally increased. Theoperations performed at block 522 are described below with reference toFIG. 5C. Process 500 then proceeds to block 506 and the above operationsare repeated using this higher current level.

Should process 500 determine at block 521 that a neural response hasbeen evoked, then at block 524 an assessment is made as to whether thereis confidence in this determination. There are many ways to evaluate theconfidence of the prediction made by the expert system operating atblock 520. In one exemplary embodiment, process 500 determines at block524 whether two consecutive stimuli have each evoked a neural response.If not, a variable ‘MaxT-NRT’ is set at block 525 to the appliedstimulus current level for use at block 522. Process 500 then proceedsto block 522 as shown in FIG. 5A. If two consecutive stimuli have eachevoked a neural response, the process 500 continues with operationsdepicted in FIG. 5B to accurately determine the minimum thresholdstimulation current which causes a neural response. These operations aredescribed in detail below with reference to FIG. 5B.

Referring now to FIG. 5C, the operations performed at block 522 in oneembodiment of the present invention are described next below. The sizeof the increment in the value of the stimulus current level depends onthe following decision tree predictions:

-   -   6 current level units if both the present and previous NRT        measurements are not deemed a neural response (blocks 582, 584        and 586).    -   4 current level units if the present NRT measurement is not        deemed a neural response whereas the previous measurement was        predicted otherwise (blocks 582, 586, 588).    -   2 current level units if the present NRT measurement is deemed a        neural response (blocks 582, 590).

If at block 524 it is determined that the T-NRT has been predicted withsufficient confidence, then process 500 continues with the descendingseries of operations illustrated in FIG. 5B. In the exemplaryembodiment, an acceptable confidence level is attained if twoconsecutive stimuli have each evoked a neural response. When thatoccurs, the stimulus current level is reset to be equal to MaxT-NRT atblock 552.

At block 554, a decision tree 600B, depicted in FIG. 6B, is applied tothe latest 32 sample set measurement of the neural response. Shoulddecision tree 600B determine that a neural response has been evoked,MaxT-NRT is set to the present stimulus current level value at block555. The stimulus current level is then decremented by 6 current levelunits at block 580. This increment size may be another value or userdefinable in alternate embodiments.

In one preferred embodiment, separate decision trees 600A and 600B areused for various phases of process 500. As described above, decisiontree 600A is used in the ascending phase illustrated in FIG. 5A, and isoptimized for a low false positive rate. Decision tree 600B, on theother hand, is used in the descending phase of process 500 illustratedin FIG. 5B, and is optimized for a low overall error rate.

At block 572 an NRT measurement is made in the same manner as thatperformed at block 510, and process 500 returns to block 554. Shoulddecision tree 600B determine that a neural response has not been evoked,the stimulus current level is reset to MaxT-NRT at block 556.

At block 558, the current level is decremented by 2 current level units,which is a smaller interval than the increment applied at block 522, andthe decrement applied at block 530.

At block 560, an NRT measurement is again performed during the CLdecrementing stage, in the same manner as the NRT measurement performedat block 510.

At block 562 the decision tree 600B is again applied to the obtained32-sample NRT measurement, in order to determine whether a neuralresponse has been evoked by application of the stimulus at the presentcurrent level. If so, the algorithm returns to step 558. At block 563MaxT-NRT is set to the present value of the stimulus current level ifdecision tree 600B has always deemed that a neural response has beenevoked since block 556.

If decision tree 600B determines that a neural response has not beenevoked, then at block 564 a determination is made as to whether twoconsecutive stimuli have not evoked a neural response. If there have notbeen two consecutive stimuli which have not evoked a neural response, avariable ‘MinT-NRT’ is set to be equal to the present value of CL, andprocess 500 returns to block 558. If there has been two consecutivestimuli which have not evoked a neural response, then at block 566 aT-NRT value is determined from the two variables of MaxT-NRT andMinT-NRT, in the manner described below with reference to FIG. 8.Process 500 then ceases at block 568.

If the current level of any stimulation pulse [probe, masker, etc.] everexceeds its range, the measurement is stopped.

In one embodiment, the algorithm is further optimised such that no NRTmeasurement is repeated at a given current level throughout thealgorithm. Previous measurements may be used if they exist for therequired current level.

FIG. 6A illustrates one embodiment of a decision tree used in theembodiment of FIG. 5A to determine whether a neural response has beenevoked. FIG. 6B illustrates one embodiment of a decision tree used inthe embodiment of FIG. 5B to determine whether a neural response hasbeen evoked. The utilization of two decision trees 600A and 600B todetermine T-NRT is advantageous is some applications. In the flowchartillustrated in FIG. 5A, the stimulation current level is incrementallyincreased and T-NRT has not yet been predicted. In such a process,decision tree 600A is utilized to provide a low false-positive rate sothat a neural response can be predicted with a high degree ofconfidence. Thereafter, while descending at finer increments in theflowchart illustrated in FIG. 5B, decision tree 600B is utilized due toits ability to more accurately predict a neural response has occurred.

Each parameter considered in decision tree structure or dichotomous key600A is defined herein below. As one of ordinary skill in the art wouldappreciate, the use of the terms attributes, parameters, features andthe like are commonly used interchangeably to refer to the raw andcalculated values utilized in a decision tree. The selection of suchterms herein, then, is solely to facilitate understanding. It shouldalso be appreciated that the first occurring peak positive and negativevalues of an NRT measurement waveform are commonly referred to as P1 andN1, respectively, as noted above. For ease of description, these termsare utilized below. In the following description, the parametersconsidered at each of the decision nodes 602, 604, 606, 608, 610 and 612are first described followed by a description of decision tree 600A.

Parameter N1P1/Noise is considered at decision node 602. ParameterN1P1/Noise represents the signal to noise ratio of the NRT measurement.As noted, in the exemplary embodiment, each NRT measurement provides atrace or waveform derived from 32 samples of the neural responseobtained at a sampling rate of 20 kHz.

-   -   N1 is the minimum of the first 8 samples.    -   P1 is the maximum of the samples after N1, up to and including        sample 16.    -   N1−P1 (μV)=ECAP_(P1)−ECAP_(N1).    -   If any of the following rules are true, N1−P1=0:        -   N1−P1<0        -   Latency between N1 and P1<2 samples        -   Latency between N1 and P1>12 samples        -   Latency between N1 and the maximum sample post-N1>15 samples            AND Ratio of N1−P1 to the range N1 onwards<0.85    -   Noise=the range (maximum minus minimum) of samples 17-32.    -   N1P1/Noise=N1−P1 (amplitude) divided by Noise (the noise level).

Parameter R_(Response) is considered at decision nodes 608 and 610.Parameter R_(Response) is defined as the correlation coefficient betweenthe given NRT measurement and a fixed good response, calculated oversamples 1-24. A predefined 32 sample standard response used in thepresent embodiment is shown in FIG. 9. In this embodiment, the standardcorrelation coefficient is utilized:

$r = \frac{\sum\limits_{Samples}{\left( {x - \overset{\_}{x}} \right)\left( {y - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{Samples}{\left( {x - \overset{\_}{x}} \right)^{2}{\sum\limits_{Samples}\left( {y - \overset{\_}{y}} \right)^{2}}}}}$

Parameter R_(Resp+Artef) is considered a decision nodes 604 and 612.Parameter R_(Resp+Artef) is defined as the correlation coefficientbetween the given NRT measurement and a fixed trace with neural responseplus artifact calculated over samples 1-24. A predefined 32 samplestandard response used in the present embodiment is shown in FIG. 10.

Parameter R_(Previous) is considered a decision node 606. ParameterR_(Previous) is defined as the correlation coefficient between the givenNRT measurement and the NRT measurement of immediately lower stimuluscurrent level, calculated over samples 1-24. In one embodiment, anypreviously performed measurement of lower stimulus level, whether thestep difference is 2CL, 6CL, etc.

As shown in FIG. 6A, when N1P1/Noise is zero, decision tree 600Apredicts that the NRT measurement does not contain a neural response asillustrated by decision node 601. Should N1P1/Noise 602 have a valuebetween 0.0 and 1.64, then the value of parameter R_(Resp+Artef) isconsidered at decision node 604. Similarly, should N1P1/Noise have avalue greater than 1.64, then the value of parameter R_(Previous) isconsidered at decision node 606.

At decision node 604 of parameter R_(Resp+Artef) is considered. If itdetermined to be less than or equal to 0.87, then parameter R_(Response)is determined at decision node 608. However, if R_(Resp+Artef) isdetermined to be greater than 0.87, then a different consideration ofparameter R_(Response) is performed at decision node 610.

Returning to decision node 606 at which parameter R_(Previous) isconsidered. If the parameter is less than or equal to 0.38, thendecision tree 600A determines that the given NRT measurement fails tocontain a neural response, as indicated at block 603 of FIG. 6A.However, if the parameter is greater than 0.38, then decision tree 600Adetermines that the given NRT measurement does contain a neuralresponse, as indicated at block 605 of FIG. 6A. Thus, if the parameterN1P1/Noise is greater than 1.64 and the parameter R_(Previous) isgreater than 0.38, then the NRT measurement is predicted to contain aneural response.

At decision node 608 decision tree 600A considered whether parameterR_(Response) is less than or equal to 0.43, in which case decision tree600A predicts that the NRT measurement does not contain a neuralresponse, as shown at block 607. At decision node 608 decision tree 600Aalso considers whether parameter R_(Response) is greater than 0.62, atwhich decision tree 600A predicts that the NRT measurement does containa neural response, as shown at block 609. Thus, if the parameterN1P1/Noise is greater than zero and less than or equal to 1.64,parameter R_(Resp+Artef) is less than or equal to 0.87 and parameterR_(Response) is less than 0.62, then decision tree 600A predicts thatthe NRT measurement contains a neural response.

At decision node 610 decision tree 600A considered whether parameterR_(Response) is less than or equal to 0.01, in which case decision tree600A predicts that the NRT measurement does not contain a neuralresponse, as shown at block 611. At decision node 610 decision tree 600Aalso considers whether parameter R_(Response) is greater than 0.01, atwhich decision tree 600A predicts that the NRT measurement does containa neural response, as shown at block 613. Thus, if the parameterN1P1/Noise is greater than zero and less than or equal to 1.64,parameter R_(Resp+Artef) is greater than 0.87, and parameterR_(Response) is greater than 0.01, then decision tree 600A predicts thatthe NRT measurement contains a neural response.

Returning to decision node 608, decision tree 600A also considerswhether parameter R_(Response) is greater than 0.43 and less than orequal to 0.62. If so, decision tree 600A considers parameterR_(Resp+Artef) at decision node 612. There, if R_(Resp+Artef) is lessthan or equal to 0.56, then decision tree 600A predicts that the NRTmeasurement does not contain a neural response, as indicated at block615. Alternatively, if R_(Resp+Atef) is greater than 0.56, then decisiontree 600A predicts that the NRT measurement contain a neural response,as indicated at block 617. Thus, if the parameter N1P1/Noise is greaterthan zero and less than or equal to 1.64, parameter R_(Resp+Artef) isless than or equal to 0.87, parameter R_(Response) is greater than 0.43and less than or equal to 0.62, and parameter R_(Resp+Artef) is greaterthan 0.56, then decision tree 600A predicts that the NRT measurementcontains a neural response.

As one or ordinary skill in the art would appreciate, the above valuesare exemplary only. For example, in one alternative embodiment, N1 isdetermined based on a quantity of sampled other than eight. Similarly,the positive peak occurs after the negative peak in NRT measurementwaveforms. In the above embodiment, the positive peak is limited to themaximum sample after the first occurring negative peak N1. However,because the trailing portion of an NRT waveform is generally level andshould not contain a pulse. It should be appreciated, however, that inalternative embodiments, P1 is defined as the maximum sample whichoccurs after N1 and less than 14-18 samples. Similarly, the latencybetween the first occurring negative and positive peaks may be otherthan 2 and 12 samples in alternative embodiments. And so on.

Referring now to FIG. 6B, decision tree 600B will be described. Theparameters or features considered or evaluated at decision blocks 652,54, 656, 658, 660, 662 and 664 are described above.

At decision node 652 parameter N1P1/Noise is considered by decision tree600B. If the parameter N1P1/Noise zero, decision tree 600B predicts thatthe NRT measurement does not contain a neural response as illustrated bydecision node 651. Should the parameter N1P1/Noise have a value greaterthan 0.0 and less than or equal to 1.41, then the value of parameterR_(Resp+Artef) is considered at decision node 654. Similarly, should theparameter N1P1/Noise have a value greater than 1.41, then the value ofparameter R_(Response) is considered at decision node 656.

At decision node 654, parameter R_(Resp+Artef) is considered. If thisparameter determined to be less than or equal to 0.87, then parameterR_(Response) is considered at decision node 660. However, ifR_(Resp+Artef) is determined to be greater than 0.87, then a differentconsideration of parameter R_(Response) is performed at decision node662.

Returning to decision node 656 at which parameter R_(Response) isconsidered. If the parameter is less than or equal to 0.57, thendecision tree 600B considers the parameter R_(Previous) at decision node658. However, if the parameter R_(Previous) is greater than 0.57, thendecision tree 600B determines that the given NRT measurement contains aneural response, as indicated at block 657 of FIG. 6B. Thus, if theparameter N1P1/Noise is greater than 1.41 and the parameter R_(response)is greater than 0.57, then the NRT measurement is predicted to contain aneural response.

Returning to decision node 658 at which parameter R_(Previous) isconsidered. If this parameter is less than or equal to 0.57, thendecision tree 600B determines that the given NRT measurement fails tocontain a neural response, as indicated at block 663 of FIG. 6B.However, if this parameter is greater than 0.57, then decision tree 600Bdetermines that the given NRT measurement does contain a neuralresponse, as indicated at block 655 of FIG. 6B. Thus, if the parameterN1P1/Noise is greater than 1.41, the parameter R_(Response) is less thanor equal to 0.57, and the parameter R_(Previous) is greater than 0.57,then the NRT measurement is predicted to contain a neural response.

At decision node 660 decision tree 600B considered whether parameterR_(Response) is less than or equal to 0.28, in which case decision tree600B predicts that the NRT measurement does not contain a neuralresponse, as shown at block 659. At decision node 608 decision tree 600Balso considers whether parameter R_(Response) is greater than 0.62, inwhich case decision tree 600B predicts that the NRT measurement doescontain a neural response, as shown at block 661. Thus, if the parameterN1 P1/Noise is greater than zero and less than or equal to 1.41,parameter R_(Resp+Artef) is less than or equal to 0.87, and parameterR_(Response) is greater than 0.62, then decision tree 600B predicts thatthe NRT measurement contains a neural response.

At decision node 662 decision tree 600B considered whether parameterR_(Response) is less than or equal to 0.013, in which case decision tree600B predicts that the NRT measurement does not contain a neuralresponse, as shown at block 667. At decision node 662 decision tree 600Balso considers whether parameter R_(Response) is greater than 0.013, inwhich case decision tree 600B predicts that the NRT measurement doescontain a neural response, as shown at block 669. Thus, if the parameterN1P1/Noise is greater than zero and less than or equal to 1.41,parameter R_(Resp+Artef) is greater than 0.87, and parameterR_(Response) is greater than 0.013, then decision tree 600B predictsthat the NRT measurement contains a neural response.

Returning to decision node 660, decision tree 600B also considerswhether parameter R_(Response) is greater than 0.43 and less than orequal to 0.62. If so, decision tree 600B considers parameterR_(Resp+Artef) at decision node 664. There, if the parameterR_(Resp+Artef) is less than or equal to 0.60, then decision tree 600Bpredicts that the NRT measurement does not contain a neural response, asindicated at block 663. Alternatively, if R_(Resp+Artef) is greater than0.60, then decision tree 600B predicts that the NRT measurement containsa neural response, as indicated at block 665. Thus, if the parameterN1P1/Noise is greater than zero and less than or equal to 1.41,parameter R_(Resp+Artef) is less than or equal to 0.87, parameterR_(Response) is greater than 0.28 and less than or equal to 0.62, andparameter R_(Resp+Artef) is greater than 0.60, then decision tree 600Bpredicts that the NRT measurement contains a neural response.

As one or ordinary skill in the art would appreciate, the above valuesare exemplary only, and that other decision trees with other parametersand decision values may be implemented.

FIG. 7 is a flow chart of one embodiment of the primary operationsperformed to optimize the artifact reduction pulse (3 e phase) and/oramplifier gain to avoid amplifier saturation in block 518 of FIG. 5A.After process 518 begins at block 702, it is initially attempted tooptimize 3 e phase with relaxed criteria at block 704.

If 3e phase optimisation does not converge (block 706) or if amplifierclipping still occurs (block 714), and process 518 continues at decisionblock 710 at which the gain is measured. If the gain is greater than 40dB, then at block 716 the gain is decreased by 10 dB and number ofsweeps is increased by a factor of 1.5. On the other hand, if the gainis not greater than 40 dB (block 710), automated T-NRT is cancelled forthe electrode.

The NRT is measured again at block 718 and amplifier clipping isevaluated at block 720. If amplifier clipping 720 still occurs, process518 returns to block 704 and the above optimization process is repeated.

Returning to block 706, if the 3e phase optimization converged (block706) and if amplifier clipping ceases (block 714), then process 518ceases at block 722. Similarly, if at block 722 amplifier clippingceases after the optimizations made at block 716, then operation 518also ceases at block 722.

FIG. 8 is a flowchart illustrating the derivation of the minimumstimulus threshold T-NRT from MaxT-NRT and MinT-NRT values, inaccordance with one embodiment of the present invention, introducedabove with reference to block 566 of FIG. 5B. In this embodiment, theminimum T-NRT level is interpolated based on the intermediate results ofthe automated T-NRT measurement.

After start block 802, process 566 advances to block 804, at which thedifference between the Maximum T-NRT and the minimum T-NRT is measured.If it is less than or equal to 10, then the result is deemed to beconfident within ±5 current levels, and a final value is output at block806. Otherwise, if a confident result cannot be determined by theautomated T-NRT algorithm, process 566 continues at block 808 at which a“?” flag is returned.

In one embodiment, the present embodiment is implemented in automatedT-NRT measurements using clinical and electrophysiological software. Inalternative embodiments, the present invention is implemented insoftware, hardware or combination thereof.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the invention as shown inthe specific embodiments without departing from the spirit or scope ofthe invention as broadly described. The present embodiments are,therefore, to be considered in all respects as illustrative and notrestrictive.

For example, in an alternative embodiment, a gain of the amplifier maybe altered between application of successive stimuli. Such embodimentsprovide for automated optimization of the gain of the amplifier tomaximize signal resolution while avoiding amplifier saturation.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is solely forthe purpose of providing a context for the present invention. It is notto be taken as an admission that any or all of these matters form partof the prior art base or were common general knowledge in the fieldrelevant to the present invention as it existed before the priority dateof each claim of this application.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

What is claimed is:
 1. A method for automatically analyzing neuralactivity within a target neural region comprising: applying electricalstimulation to the target neural region at an initial current level thatapproximates a typical threshold-Neural Response Telemetry (NRT) level;recording an NRT measurement of neural activity within the target neuralregion in response to the stimulation; and utilizing a machine-learnedexpert system configured with a decision tree that includes at least twolevels of nodes which consider parameters relating to the NRTmeasurement, respectively, so as to predict, based on one or morefeatures of the neural activity, whether the NRT measurement includes aneural response or does not include a neural response, the step ofutilizing including: evaluating at least two successive ones of thelevels of nodes in order to predict whether the NRT measurement includesa neural response or does not include a neural response, wherein atleast one of the evaluated levels includes a node which considers acorrelation coefficient relating the NRT measurement to a predeterminedvalue.
 2. The method of claim 1, further comprising: selecting theinitial current level to insure safety while minimizing a quantity ofmeasurements required to determine the threshold-NRT (T-NRT) level ofthe target neural region.
 3. The method of claim 2, wherein selectingthe initial current level comprises: in intra-operative applications,selecting as an initial current level a current level at which a neuralresponse is not expected to be evoked.
 4. The method of claim 3, whereinselecting the initial current level comprises: selecting an initialcurrent level substantially below the typical T-NRT.
 5. The method ofclaim 2, wherein selecting the initial current level comprises: inintra-operative applications, selecting an initial current level that isabove or below the typical T-NRT.
 6. The method of claim 1, wherein whenthe machine-learned expert system predicts that the NRT measurement doesnot contain a neural response, the method further comprises:incrementing the current level of the electrical stimulus; repeating theapplying, recording and utilizing steps; and repeating the incrementingand repeating steps until a neural response has been evoked.
 7. Themethod of claim 1, further comprising: locally establishingthreshold-NRT (T-NRT) at a finer stimulus resolution.
 8. The method ofclaim 1, wherein when the machine-learned expert system predicts thatthe NRT measurement does not contain a neural response, the methodfurther comprises: decreasing the current level of the electricalstimulus; repeating the applying, recording and utilizing steps; andrepeating the decreasing and repeating steps until a neural response hasbeen evoked.
 9. The method of claim 1, wherein at least one of theevaluated levels in the decision tree includes at least one node whichrespectively considers one or more of: a parameter relating to a ratioof the peak-to-peak amplitude of the measurement to the noise of themeasurement; a parameter relating to a correlation of the measurement toa predefined expected neural response; a parameter relating to thecorrelation of the measurement to a predefined expected trace containingneural response plus stimulus artifact; a parameter relating to thecorrelation of the measurement to a predefined expected trace containingstimulus artifact only; a parameter relating to the correlation of themeasurement to a previous measurement with neural stimulus of similarlevel; and a parameter relating to a stimulus current level.
 10. Acochlear implant system comprising: a cochlear implant configured tocommunicate with an external device; an automatic neural responsemeasurement system, communicably coupled to the cochlear implant,configured to record a Neural Response Telemetry (NRT) measurement ofneural activity within a cochlea in response to electrical stimulationof the cochlea by electrodes of the cochlear implant, wherein an initialcurrent level of electrical stimulation applied to evoke neural activityis set to a level which approximates a typical threshold-NRT level; andan expert system configured with a decision tree that includes at leasttwo levels of nodes which consider parameters relating to the NRTmeasurement, respectively, so as to determine, based on one or morefeatures of the evoked neural activity, whether the NRT measurementincludes a neural response or does not include a neural response, theexpert system being further configured with a machine learning algorithmoperable to evaluate at least two successive ones of the levels of nodesin order to predict whether the NRT measurement includes a neuralresponse or does not include neural response, at least one of theevaluated levels including a node which considers a correlationcoefficient relating the NRT measurement to a predetermined value. 11.The cochlear implant system of claim 10, wherein at least one of theevaluated levels in the decision tree includes at least one node whichrespectively considers one or more of: a parameter relating to the ratioof the peak-to-peak amplitude of the measurement to the noise of themeasurement; a parameter relating to the correlation of the measurementto a predefined expected neural response; a parameter relating to thecorrelation of the measurement to a predefined expected trace containingneural response plus stimulus artifact; a parameter relating to thecorrelation of the measurement to a predefined expected trace containingstimulus artifact only; a parameter relating to the correlation of themeasurement to a previous measurement with neural stimulus of similarlevel; and a parameter relating to a stimulus current level.
 12. Amethod for determining a minimum stimulus threshold at which a neuralresponse is evoked, comprising: applying a neural stimulus; obtaining aNeural Response Telemetry (NRT) measurement of neural activityresponsive to the neural stimulus; determining whether the neuralstimulus has or has not evoked a neural response by applying an expertsystem to the NRT measurement, wherein the expert system considers oneor more features extracted from the NRT measurement including a signalto noise ratio thereof, comprising: evaluating at least two successivelevels of nodes of a decision tree in order to predict whether theneural stimulus has or has not evoked a neural response, wherein atleast one of the evaluated levels includes a node which considers acorrelation coefficient relating the NRT measurement to a predeterminedvalue.
 13. The method of claim 12, wherein the neural stimulus isapplied to a target neural region at an initial current level thatapproximates a typical threshold-Neural Response Telemetry (NRT) level,the method further comprising: selecting the initial current level toinsure safety while minimizing a quantity of measurements required todetermine a threshold-NRT level of the target neural region.
 14. Themethod of claim 13, wherein selecting the initial current levelcomprises: selecting as an initial current level a current level atwhich a neural response is not expected to be evoked.
 15. The method ofclaim 14, wherein selecting the initial current level comprises:selecting an initial current level substantially below the typicalT-NRT.
 16. The method of claim 13, wherein selecting the initial currentlevel comprises: selecting an initial current level that is at least oneof above and below the typical T-NRT.
 17. The method of claim 12,wherein when the expert system predicts that the measurement does notcontain a neural response, the method further comprises: incrementingthe current level of the neural stimulus; repeating the applying,obtaining and evaluating steps; and repeating the incrementing andrepeating steps until a neural response has been evoked.
 18. The methodof claim 12, wherein when the expert system predicts that themeasurement does not contain a neural response, the method furthercomprises: decreasing the current level of the neural stimulus;repeating the applying, obtaining and evaluating steps; and repeatingthe decreasing and repeating steps until a neural response has beenevoked.
 19. An expert system configured to determine whether a NeuralResponse Telemetry (NRT) measurement contains a neural response or doesnot contain a neural response, wherein the NRT measurement having beenmade in response to a cochlear implant (CI) stimulus, the expert systemcomprising a machine learning algorithm using the induction of at leastone decision tree having at least two levels of nodes, wherein the nodesof the decision tree consider, respectively: a parameter relating to theratio of the peak-to-peak amplitude of the measurement to the noise ofthe measurement; and one or more of: a parameter relating to thecorrelation of the NRT measurement to a predefined expected neuralresponse; a parameter relating to the correlation of the NRT measurementto a predefined expected trace containing neural response plus CIstimulus artifact; a parameter relating to the correlation of the NRTmeasurement to a predefined expected trace containing CI stimulusartifact only; and a parameter relating to the correlation of the NRTmeasurement to a previous measurement with CI stimulus of similar level.20. A system for analyzing neural activity within a target neural regioncomprising: means for applying electrical stimulation to the targetneural region at an initial current level that approximates a typicalthreshold-Neural Response Telemetry (NRT) level; means for recording anNRT measurement of neural activity within the target neural region inresponse to the stimulation; and means for predicting whether the NRTmeasurement contains a neural response or does not contain a neuralresponse, wherein the means for predicting include an expert systemconfigured to consider one or more features of the NRT measurementincluding a signal to noise ratio thereof and to evaluating at least twosuccessive levels of nodes of a decision tree in order to predictwhether the NRT measurement does or does not contain a neural response,wherein at least one of the evaluated levels includes a node whichconsiders a correlation coefficient relating the NRT measurement to apredetermined value.
 21. The system of claim 20, further comprising:means for selecting the initial current level to insure safety whileminimizing a quantity of measurements required to determine thethreshold NRT.
 22. The system of claim 21, wherein the means forselecting the initial current level, in intra-operative applications,selects as an initial current level a current level at which a neuralresponse is not expected to be evoked.
 23. The system of claim 22,wherein the means for selecting the initial current level selects aninitial current level substantially below the typical T-NRT.
 24. Thesystem of claim 21, wherein the means for selecting the initial currentlevel, in intra-operative applications, selects an initial current levelthat is above or below the typical T-NRT.